{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25da6c50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Init Plugin\n",
      "Init Graph Optimizer\n",
      "Init Kernel\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pathlib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "186df3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir='../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d881628",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root=pathlib.Path(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f585ff07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_root"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "475c7004",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake\n",
      "../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane\n"
     ]
    }
   ],
   "source": [
    "for item in data_root.iterdir():\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ce50ce55",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_image_path=list(data_root.glob('airplane/*'))+list(data_root.glob('lake/*'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b1bcf965",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1400"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(all_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59c9ac5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_079.jpg'),\n",
       "  PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_045.jpg'),\n",
       "  PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_051.jpg')],\n",
       " [PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_039.jpg'),\n",
       "  PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_011.jpg'),\n",
       "  PosixPath('../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_005.jpg')])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[:3],all_image_path[-3:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e68e3bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_image_path=[str(path) for path in all_image_path]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7cdc1b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_092.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_509.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_521.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_247.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_253.jpg']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[4:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1eefb86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "166daa3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "random.shuffle(all_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d78ce291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_276.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_207.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_360.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_246.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_290.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_072.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_236.jpg']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[2:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "72c287f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1400"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_count=len(all_image_path)\n",
    "image_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "27023172",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['airplane', 'lake']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names= sorted([item.name for item in data_root.glob('*')])\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a0cb6c8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_to_index=dict((name,index) for index ,name in enumerate(label_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a1b5be0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'airplane': 0, 'lake': 1}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_to_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4917c6f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lake'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathlib.Path(\"../Desktop/data/2_class/lake/lake_651.jpg\").parent.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83aeb036",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_image_label=[label_to_index[pathlib.Path(p).parent.name] for p in all_image_path ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4b51ff22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 0, 0, 0, 0]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_label[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dbb28824",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_438.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_580.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_276.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_207.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_360.jpg',\n",
       " '../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/airplane/airplane_246.jpg']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_image_path[:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "438d6892",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_to_label= dict((v,k) for k,v in label_to_index.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0eec542d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 'airplane', 1: 'lake'}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index_to_label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed76cde",
   "metadata": {},
   "source": [
    "# 解码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "108d17bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import IPython.display as display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "75ae2c2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lake\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "airplane\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for n in range(3):\n",
    "    image_index=random.choice(range(len(all_image_path)))\n",
    "    display.display(display.Image(all_image_path[image_index]))\n",
    "    print(index_to_label[all_image_label[image_index]])\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2ac6ddb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../Desktop/Tenosrflow2.0教程-日月光华（不加密）/日月光华-tensorflow资料/data/2_class/lake/lake_438.jpg'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_path=all_image_path[0]\n",
    "image_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b835ba69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-01 12:42:05.560838: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2021-08-01 12:42:05.560913: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "data": {
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      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_raw=tf.io.read_file(image_path)\n",
    "image_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "585906e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([256, 256, 3])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_tensor=tf.image.decode_image(image_raw)\n",
    "image_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2de77f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tf.uint8"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_tensor.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ee01463c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(256, 256, 3), dtype=uint8, numpy=\n",
       "array([[[ 67,  66,  62],\n",
       "        [ 51,  50,  46],\n",
       "        [ 70,  69,  64],\n",
       "        ...,\n",
       "        [106,  99,  89],\n",
       "        [117, 108,  99],\n",
       "        [125, 116, 107]],\n",
       "\n",
       "       [[ 92,  91,  87],\n",
       "        [ 75,  74,  70],\n",
       "        [ 77,  76,  71],\n",
       "        ...,\n",
       "        [ 93,  86,  76],\n",
       "        [113, 104,  95],\n",
       "        [121, 112, 103]],\n",
       "\n",
       "       [[118, 117, 112],\n",
       "        [106, 105, 100],\n",
       "        [ 89,  88,  83],\n",
       "        ...,\n",
       "        [ 77,  70,  60],\n",
       "        [105,  96,  87],\n",
       "        [117, 108,  99]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 65,  66,  48],\n",
       "        [ 64,  65,  47],\n",
       "        [ 65,  68,  51],\n",
       "        ...,\n",
       "        [ 56,  69,  52],\n",
       "        [ 58,  69,  55],\n",
       "        [ 59,  70,  54]],\n",
       "\n",
       "       [[ 63,  64,  46],\n",
       "        [ 67,  68,  50],\n",
       "        [ 74,  75,  57],\n",
       "        ...,\n",
       "        [ 53,  66,  48],\n",
       "        [ 56,  67,  51],\n",
       "        [ 60,  71,  54]],\n",
       "\n",
       "       [[ 64,  65,  47],\n",
       "        [ 73,  74,  56],\n",
       "        [ 82,  83,  65],\n",
       "        ...,\n",
       "        [ 48,  61,  43],\n",
       "        [ 50,  61,  44],\n",
       "        [ 55,  66,  49]]], dtype=uint8)>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_tensor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9d309eb",
   "metadata": {},
   "source": [
    "# 10 tf.data构造输入（9的基础上）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "68156b51",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_tensor=tf.cast(image_tensor,tf.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0af8eef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(256, 256, 3), dtype=float32, numpy=\n",
       "array([[[0.2627451 , 0.25882354, 0.24313727],\n",
       "        [0.20000002, 0.19607845, 0.18039216],\n",
       "        [0.27450982, 0.27058825, 0.2509804 ],\n",
       "        ...,\n",
       "        [0.4156863 , 0.38823533, 0.34901962],\n",
       "        [0.45882356, 0.42352945, 0.38823533],\n",
       "        [0.4901961 , 0.454902  , 0.41960788]],\n",
       "\n",
       "       [[0.36078432, 0.35686275, 0.34117648],\n",
       "        [0.29411766, 0.2901961 , 0.27450982],\n",
       "        [0.3019608 , 0.29803923, 0.2784314 ],\n",
       "        ...,\n",
       "        [0.3647059 , 0.3372549 , 0.29803923],\n",
       "        [0.4431373 , 0.40784317, 0.37254903],\n",
       "        [0.47450984, 0.43921572, 0.4039216 ]],\n",
       "\n",
       "       [[0.46274513, 0.45882356, 0.43921572],\n",
       "        [0.4156863 , 0.41176474, 0.3921569 ],\n",
       "        [0.34901962, 0.34509805, 0.3254902 ],\n",
       "        ...,\n",
       "        [0.3019608 , 0.27450982, 0.23529413],\n",
       "        [0.41176474, 0.37647063, 0.34117648],\n",
       "        [0.45882356, 0.42352945, 0.38823533]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[0.25490198, 0.25882354, 0.18823531],\n",
       "        [0.2509804 , 0.25490198, 0.18431373],\n",
       "        [0.25490198, 0.26666668, 0.20000002],\n",
       "        ...,\n",
       "        [0.21960786, 0.27058825, 0.20392159],\n",
       "        [0.227451  , 0.27058825, 0.21568629],\n",
       "        [0.23137257, 0.27450982, 0.21176472]],\n",
       "\n",
       "       [[0.24705884, 0.2509804 , 0.18039216],\n",
       "        [0.2627451 , 0.26666668, 0.19607845],\n",
       "        [0.2901961 , 0.29411766, 0.22352943],\n",
       "        ...,\n",
       "        [0.20784315, 0.25882354, 0.18823531],\n",
       "        [0.21960786, 0.2627451 , 0.20000002],\n",
       "        [0.23529413, 0.2784314 , 0.21176472]],\n",
       "\n",
       "       [[0.2509804 , 0.25490198, 0.18431373],\n",
       "        [0.28627452, 0.2901961 , 0.21960786],\n",
       "        [0.32156864, 0.3254902 , 0.25490198],\n",
       "        ...,\n",
       "        [0.18823531, 0.2392157 , 0.16862746],\n",
       "        [0.19607845, 0.2392157 , 0.17254902],\n",
       "        [0.21568629, 0.25882354, 0.19215688]]], dtype=float32)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_tensor=image_tensor/255\n",
    "image_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8c71007c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.0, 0.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_tensor.numpy().max(),image_tensor.numpy().min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dd45fead",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_preprosess_image(image_path):\n",
    "    image_raw=tf.io.read_file(image_path)\n",
    "    image_tensor=tf.image.decode_jpeg(image_raw,channels=3)\n",
    "    image_tensor=tf.image.resize(image_tensor,[256,256])\n",
    "    image_tensor=tf.cast(image_tensor,tf.float32)\n",
    "    image=image_tensor/255\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "34b7ce55",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path1=all_image_path[900]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ceed75e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x165ee2550>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(load_preprosess_image(image_path1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ad539270",
   "metadata": {},
   "outputs": [],
   "source": [
    "path_ds=tf.data.Dataset.from_tensor_slices(all_image_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9fc132eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<TensorSliceDataset shapes: (), types: tf.string>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "33a47719",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_dataset=path_ds.map(load_preprosess_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6a7cbd56",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_dataset=tf.data.Dataset.from_tensor_slices(all_image_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f58504dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "1\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "for label in label_dataset.take(10):\n",
    "    print(label.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d56378cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.2627451  0.25882354 0.24313726]\n",
      "  [0.2        0.19607843 0.18039216]\n",
      "  [0.27450982 0.27058825 0.2509804 ]\n",
      "  ...\n",
      "  [0.41568628 0.3882353  0.34901962]\n",
      "  [0.45882353 0.42352942 0.3882353 ]\n",
      "  [0.49019608 0.45490196 0.41960785]]\n",
      "\n",
      " [[0.36078432 0.35686275 0.34117648]\n",
      "  [0.29411766 0.2901961  0.27450982]\n",
      "  [0.3019608  0.29803923 0.2784314 ]\n",
      "  ...\n",
      "  [0.3647059  0.3372549  0.29803923]\n",
      "  [0.44313726 0.40784314 0.37254903]\n",
      "  [0.4745098  0.4392157  0.40392157]]\n",
      "\n",
      " [[0.4627451  0.45882353 0.4392157 ]\n",
      "  [0.41568628 0.4117647  0.39215687]\n",
      "  [0.34901962 0.34509805 0.3254902 ]\n",
      "  ...\n",
      "  [0.3019608  0.27450982 0.23529412]\n",
      "  [0.4117647  0.3764706  0.34117648]\n",
      "  [0.45882353 0.42352942 0.3882353 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.25490198 0.25882354 0.1882353 ]\n",
      "  [0.2509804  0.25490198 0.18431373]\n",
      "  [0.25490198 0.26666668 0.2       ]\n",
      "  ...\n",
      "  [0.21960784 0.27058825 0.20392157]\n",
      "  [0.22745098 0.27058825 0.21568628]\n",
      "  [0.23137255 0.27450982 0.21176471]]\n",
      "\n",
      " [[0.24705882 0.2509804  0.18039216]\n",
      "  [0.2627451  0.26666668 0.19607843]\n",
      "  [0.2901961  0.29411766 0.22352941]\n",
      "  ...\n",
      "  [0.20784314 0.25882354 0.1882353 ]\n",
      "  [0.21960784 0.2627451  0.2       ]\n",
      "  [0.23529412 0.2784314  0.21176471]]\n",
      "\n",
      " [[0.2509804  0.25490198 0.18431373]\n",
      "  [0.28627452 0.2901961  0.21960784]\n",
      "  [0.32156864 0.3254902  0.25490198]\n",
      "  ...\n",
      "  [0.1882353  0.23921569 0.16862746]\n",
      "  [0.19607843 0.23921569 0.17254902]\n",
      "  [0.21568628 0.25882354 0.19215687]]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-01 12:42:05.726802: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-08-01 12:42:05.726993: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    }
   ],
   "source": [
    "for image in image_dataset.take(1):\n",
    "    print(image.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3801a4b",
   "metadata": {},
   "source": [
    "# 输入管道"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2306969c",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=tf.data.Dataset.zip((image_dataset,label_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ee49a1ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ZipDataset shapes: ((256, 256, 3), ()), types: (tf.float32, tf.int32)>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e5c4ffa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=dataset.take(int(image_count*0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "95a9e065",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset=dataset.skip(int(image_count*0.8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d3dbee42",
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE=32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6cbbbebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset=train_dataset.shuffle(int(image_count*0.8)).batch(BATCH_SIZE)#不写repeat代表无限循环"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7a16bcae",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset=test_dataset.batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef2a62c",
   "metadata": {},
   "source": [
    "# 构造模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "81504804",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential()   #顺序模型\n",
    "model.add(tf.keras.layers.Conv2D(64, (3, 3), input_shape=(256, 256, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.Conv2D(256, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(512, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(512, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.MaxPooling2D())\n",
    "model.add(tf.keras.layers.Conv2D(1024, (3, 3), activation='relu'))\n",
    "model.add(tf.keras.layers.GlobalAveragePooling2D())\n",
    "model.add(tf.keras.layers.Dense(1024, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
    "model.add(tf.keras.layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e505baee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 254, 254, 64)      1792      \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 252, 252, 64)      36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 126, 126, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 124, 124, 128)     73856     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 122, 122, 128)     147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 61, 61, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 59, 59, 256)       295168    \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 57, 57, 256)       590080    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 26, 26, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 13, 13, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 11, 11, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 5, 5, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_8 (Conv2D)            (None, 3, 3, 1024)        4719616   \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               262400    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 10,717,249\n",
      "Trainable params: 10,717,249\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a6b692d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "             loss='binary_crossentropy',\n",
    "             metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f3a209b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps_per_epoch=int(image_count*0.8)//BATCH_SIZE\n",
    "validation_steps=(image_count-int(image_count*0.8))//BATCH_SIZE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05d6d65b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-01 13:28:12.440869: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - ETA: 0s - loss: 0.6934 - acc: 0.5652"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-01 13:30:45.090941: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 181s 5s/step - loss: 0.6934 - acc: 0.5652 - val_loss: 0.5349 - val_acc: 0.9062\n",
      "Epoch 2/5\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(train_dataset,epochs=1,#电脑太慢了\n",
    "                  steps_per_epoch=steps_per_epoch,\n",
    "                 validation_data=test_dataset,\n",
    "                 validation_steps=validation_steps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d9e0f44",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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